class_docstr: EfficientNet head: 1x1, BN, AF, AvgPool, Dropout, FC.
code: def complexity(cx, w_in, w_out, num_classes):
      cx = conv2d_cx(cx, w_in, w_out, 1)
      cx = norm2d_cx(cx, w_out)
      cx = gap2d_cx(cx, w_out)
      cx = linear_cx(cx, w_out, num_classes, bias=True)
      return cx
  
imports: from pycls.core.config import cfg
  from pycls.models.blocks import activation, conv2d, conv2d_cx, drop_connect, gap2d, gap2d_cx, init_weights, linear, linear_cx, norm2d, norm2d_cx, SE
  from torch.nn import Dropout, Module
label_desc: Use image node_id 3 for calling the EffHead obj's underlying member method code with example usage: obj.complexity(cx, w_in, w_out, num_classes) and returns: cx